Student Työpaikat Finland

Samuel Kaski’s two-part research lab in ELLIS Institute Finland (, Aalto University) and the in University of Manchester, is searching for postdocs and doctoral students to work on AI fundamentals in exciting projects. The work includes collaboration within , the (FCAI), with the rest of , and researchers from other fields.

Samuel Kaski is Professor of Computer Science in Aalto University and Professor of AI in the University of Manchester. He is the Director of and the . His research group develops machine learning principles and methods focusing on a few key topics, often working with researchers of other fields in new exciting applications (see currently available topics below).

Topics

You will join a team developing the next generation of probabilistic and collaborative AI. We study fundamental questions in machine learning, including uncertainty-aware and simulation-based inference, generative modeling, robustness under distribution shift, automatic experimental design, privacy-preserving learning, (inverse) reinforcement learning, computational rationality, and user modelling. Our goal is to develop principled AI methods that are reliable, adaptive, and scientifically useful. The research combines advances in ML foundations with real-world applications in domains such as scientific discovery, healthcare, and design or drugs, materials, systems. By bringing together expertise in machine learning, statistics, optimization, we tackle challenging interdisciplinary problems that cannot be solved by any single approach alone. Below, we outline the research topics for which we are currently seeking candidates.

Multimodal foundation models

Key words: multimodal learning, foundation models, human-aligned fine-tuning, fine-tuning for downstream tasks, test-time adaptation

You will join a research team developing next-generation multimodal foundation models that can reason across text, images, video, and 3D molecular design and robotic environments. The research is conducted within an EU-funded European AI initiative . Our goal is to make these systems more grounded, adaptable, efficient, and aligned with human goals and feedback. The work combines fundamental advances in multimodal representation learning with practical questions of deploying large-scale AI systems in dynamic real-world settings.

Depending on your interests, you may work on topics such as large-scale multimodal training, test-time adaptation under distribution shift, efficient model distillation and adaptation, retrieval-augmented learning, or alignment through human feedback, preference, and interaction.

Out-of-Distribution Deployable Machine Learning

Key words: out-of-distribution generalization, distribution shift, active learning, human-in-the-loop learning, probabilistic modelling, sequential experimental design, collaborative AI, decision support

We develop machine learning methods that remain reliable when deployed outside their training conditions. A central challenge in modern AI is that real-world environments differ from the data that models were trained on, leading to failures caused by distribution shifts, hidden confounders, and incorrect assumptions. Our ERC AdG-funded research addresses these challenges by combining probabilistic machine learning, adaptive inference, and human-collaborative AI.

Your work will focus on developing algorithms and frameworks that enable models to adapt to new environments, learn efficiently from limited feedback, and support human decision-making under uncertainty. Depending on your interests, the research may involve out-of-distribution generalization, domain adaptation, active learning, learning from expert feedback, sequential experimental design, collaborative AI systems, or probabilistic approaches to robust deployment. The project combines foundational ML research with opportunities to collaborate closely with leading application-domain experts and international research partners.

Collaborative AI

Key words: collaborative AI, human–AI interaction, decision support, human-in-the-loop learning, uncertainty-aware AI, interactive machine learning, computational rationality, AI-assisted discovery

You will join a research team developing collaborative AI systems that work effectively with people in complex decision-making and problem-solving tasks. Our goal is to build AI methods that can interact naturally with users, reason under uncertainty, adapt to human preferences and expertise, and support reliable human decision-making. The research combines machine learning, probabilistic modelling, cognitive modelling, and interactive AI to develop systems that complement rather than replace human intelligence.

You may work on topics such as human-in-the-loop learning, uncertainty-aware decision support, preference learning, adaptive interaction, AI-assisted scientific discovery, computational rationality, or collaborative reasoning between humans and AI systems. The work addresses both foundational questions in human-centered machine learning and practical challenges in deploying collaborative AI in real-world environments. The work offers opportunities to collaborate with leading international researchers and application-domain experts in areas including healthcare, sciences, and intelligent decision support.

Fundamental and Applied Machine Learning Research

Key words: machine learning, probabilistic modelling, generative AI, representation learning, optimization, trustworthy AI, adaptive systems, AI for science

We are also looking for researchers interested in tackling ambitious open problems in machine learning beyond the themes listed above. Our group works on a broad range of topics spanning probabilistic modelling, generative AI, adaptive and interactive learning systems, trustworthy AI, and AI methods for scientific discovery and decision-making. We are particularly interested in research that combines strong methodological foundations with the potential for high real-world impact - and the impact can come at different time horizons.

Depending on your background and interests, your work may involve developing new machine learning principles, scalable inference and optimization methods, robust and uncertainty-aware AI systems, generative models, representation learning methods, or novel applications of AI in science, healthcare, and intelligent systems. We encourage interdisciplinary research and collaboration across machine learning, statistics, cognitive science, and application domains. The position offers considerable freedom to shape research directions while contributing to a collaborative and internationally connected research environment.

Your experience and ambitions

We expect the candidates to have a solid background in the mathematics/statistics/computer science needed in machine learning, and hold or be close to getting a relevant doctoral degree for a postdoctoral researcher and a master degree for a doctoral student researcher.

Previous experience in the application fields and cognitive science is an advantage. Capability of both independent work and teamwork, and excellent written and spoken English are necessary.

We provide

1) RESEARCH ENVIRONMENT

You will work in Professor Samuel Kaski’s research group in ELLIS Institute Finland ( or the UK (). We design collaborations as we go, according to what the research needs. Collaborators include but are not restricted to the other groups in , the Finnish Center for Artificial Intelligence (), other sites of the European Laboratory for Learning and Intelligent Systems () and of the University of Manchester and a number of excellent researchers in other fields in our applications.

2) JOB DETAILS

Postdoc positions are typically made for up to three years, with option for renewal; doctoral-student positions start with a two-year contract and continue with a second two-year contract after a check-point. Starting dates are flexible and all positions are negotiated on an individual basis. We are strongly committed to offering everyone an inclusive and non-discriminating working environment. We warmly welcome qualified candidates from all backgrounds to apply and particularly encourage applications from women and other groups underrepresented in the field.

All our positions are fully funded and the salary is based on the Finnish universities’ pay scale. The starting salary depends on the level of the position and the previous experience and is typically starting from 4200€ for postdocs and 3100€ for doctoral students, increased as the experience grows. All employees have access to occupational health care services and are covered by the Finnish national health insurance system.